There are nearly 40 million adults in the US that have one of the sight-threatening eye diseases [1]. Direct medical costs in the United States due to the major eye diseases are estimated at over $14.5 billion [2]. The major eye diseases affect a person's quality of life. By 2050, the estimated number of people affected by eye diseases is expected to double [3,4]. With the growing increase in prevalence of eye diseases and shortage of eyecare specialists, automatic screening and detection of these pathologies is becoming vital. Savings from early detection and treatment through screening programs are widely documented [5,6]. Automatic retinal screening software gives patients access to eyecare at locations other than the specialist's clinics. A major hurdle to successfully implementing automatic screening is the need for scalability such that a single software program can be applied to images from any fundus camera and in the detection of any retinal disease.
Automatic (computer-based) screening at the point of service, promises to address the problems of access and compliance. However, this promise has not been realized in large part due to a two-way market segmentation. One, the market is divided into several retinal camera brands and models, resulting in a wide spectrum of image characteristics. Two, there is a selection of automatic screening algorithms that operate not only with a specific camera, but also for a single eye disease, resulting in a significant interoperability problem. Scaling to meet the demand for eye screening across images from a plurality of fundus cameras, for a plurality of retinal diseases, and automatic screening software represents a major hurdle.
In a recent study Tufail et al. [7] tested three diabetic retinopathy (DR) automatic screening algorithms developed by various groups [8,9,10] on retinal images obtained in a National Health Service setting in the UK. The performance of the algorithms was less effective than their previously published performance in other datasets [9,10]. This notable degradation in performance can be attributed to differences in the image characteristics (camera) used in the training set for their respective models and the images (from different model cameras) used to test the three algorithms, among other factors. The algorithms need an expensive retraining phase to deal with the distinct characteristics of different camera models.
Deep learning (DL) algorithms have been proposed for addressing the need for scaling and enhancing automatic screening algorithms. There are a variety of challenges inherent to the design of deep learning networks. For example, when designing deep learning networks, one must choose the number of layers and decide on their architecture. During training, hyperparameters, such as the learning rate, batch size, and iterations must also be chosen. These requirements incur the challenge of expensive and extensive training iterations. Multiscale amplitude-modulation frequency modulation (AM-FM) system and methods of amplitude-modulation frequency-modulation (AM-FM) demodulation for image and video processing. U.S. Pat. No. 8,515,201 B1. Methods have been shown to be successful in producing comprehensive sets of features for discriminating between normal and pathological retinal images [11], and so, shall be used to expedite the learning process.
DL has been applied in ophthalmology [12,13,14], but not in a manner as taught herein. The value and capabilities of DL are being recognized in the area of computer-based medical image classification for detection of pathology in various imaging modalities. These studies demonstrate that DL approaches yield better performance than traditional image processing and classification methods [13,15].
Abramoff et al., reported using DL to detect DR [13]. Their database consisted of 1,748 images obtained from one camera and applied to only one retinal disease, DR. Although the results of this study are encouraging, this study focuses on only one camera model and does not address a plurality of retinal disease detection. Recently, a study published by Gulshan et al., [16] used DL for DR detection, which used 128,175 images from six different types of cameras for training Said study does not introduce a camera not used in the training to test the scalability of their DL methodology.
An autoencoder is an artificial neural network (ANN) model that learns parameters for a series of nonlinear transformations that encodes the input into a lower dimensional space or feature set and decodes a reconstructed output, with the goal of minimizing the differences between the original input and the decoded output. An autoencoder produces an intermediate representation of the input that minimizes information loss [17,18]. The encoding process transforms the input data into a different data representation suitable for computer processing but not for human mental processes due to the volume, complexity (high dimensionality) and timeframe. The decoding process by a computer processor the decoding process simply reconstructs the input to the encoder, with some loss, and may or mot not be suitable for human analyses. The complexity of the encoding/decoding process and the speed required to generate results that are compatible with current clinical flow prohibit a human from processing the same volume of data of the same complexity during the same time period.
The extraction of features from a retinal image is commonly the basis for most automatic classification system. Morphological methods [19], Gabor filters [20], Wavelet transforms [21,22] and Match filters [23, 24] are the most popular methods for the feature extraction. The latter have been widely used on retinal images for vessels segmentation. ANN like stacked autoencoders or convolutional neural networks are designed to learn useful filters, which bring out key features from the image. Combining these learning methods with the AM-FM filters will enhance the feature space and accelerate the autoencoding process.